1 License

Code copyright the author (2022)

License:

CC-BY

To cite:

Anderson, B, (2022) New Zealand generation share trends: Electricity Authority data. https://dataknut.github.io/gridCarbon/docs/nzGenMixTrends.html

Code:

https://github.com/dataknut/gridCarbon/blob/master/rmd/nzGenMixTrends.Rmd

2 Introduction

Inspired by https://twitter.com/DrSimEvans/status/1508409309775994892

3 Data

This analysis uses the New Zealand electricity Authority Generation data.

The data contains average (mean) MW generation per half hour by source including. Where necessary we convert this to MWh per half hour (MWh = mean half-hourly MW/2)

As far as we can work out this data does not include distributed (i.e. non-grid connected) generation such as small scale wind, solar, hydro, biomass etc which is connected to the LV network. This means the EA data is likely to underestimate total generation and potentially underestimate the proportion of total generation that is renewable. It is possible that this could be fixed using embedded wind & solar generation data from the metered embedded generation data although this may still not include household level (micro)generation.

The data also includes a half-hourly carbon intensity value in g CO2/kWh sourced from https://www.carbonintensity.org.uk/.

rmdParams$olderThan <- 7

If the data we have previously downloaded is more than 7` days old, re-download.

rmdParams$dataPath <- "~/Dropbox/data/NZ_ElecAuth/processed/"

# load all the files we have using rbindlist
# probably ought to do some filtering here
filesToLoad <- list.files(rmdParams$dataPath, pattern = ".csv", 
                          full.names = TRUE)

orig_DT <- rbindlist(lapply(filesToLoad, fread))
setnames(orig_DT, "rDateTime", "dv_dateTime")

message("Original data range from: ", min(orig_DT$dv_dateTime, na.rm = TRUE))

## Original data range from: 2020-01-01 00:15:00

message("...to: ", max(orig_DT$dv_dateTime , na.rm = TRUE))

## ...to: 2024-05-31 23:45:00

# make wide for ease of aggregation ----
nzGenMix_wide_dt <- dcast(orig_DT[!is.na(dv_dateTime)], dv_dateTime ~ Fuel_Code, value.var = "kWh", fun.aggregate = sum)

# add derived variables used later ----
addDerivedVars <- function(dt){
  dt[, dv_year := lubridate::year(dv_dateTime)]
dt[, dv_date := lubridate::as_date(dv_dateTime)]
dt[, dv_month := lubridate::month(dv_dateTime)]
dt[, dv_hour := lubridate::hour(dv_dateTime)]
dt[, dv_hms := hms::as_hms(dv_dateTime)]
dt <- gridCarbon::add_season(dt, 
                                 dateVar = "dv_dateTime",
                                 h = "S")
dt <- gridCarbon::add_peakPeriod(dt, 
                                      dateTime = "dv_dateTime")
  return(dt)
}

nzGenMix_wide_dt <- addDerivedVars(nzGenMix_wide_dt)
nzGenMix_dt <- addDerivedVars(orig_DT[!is.na(dv_dateTime)])

#message("Remove incomplete years to avoid weird things in plots.")

message("Filtered data range from: ", min(nzGenMix_wide_dt$dv_dateTime))

## Filtered data range from: 2020-01-01 00:15:00

message("...to: ", max(nzGenMix_wide_dt$dv_dateTime))

## ...to: 2024-05-31 23:45:00

rmdParams$plotCap <- paste0("Data: NZ EA Generation ",
                   min(nzGenMix_wide_dt$dv_date), " - ",
                   max(nzGenMix_wide_dt$dv_date),
                            "\nPlot: @dataknut \nCode: https://github.com/dataknut/gridCarbon")

Note: * the data covers more years than we need * the data may contain partial years - BEWARE incomplete years in plots using annual totals or means across all months.

4 Recreating DrSimEvans’ plot

This looks like daily data.

Solar + wind (% of gen) vs coal + gas (& of gen))

# add together the % and totals we want (half-hourly)

nzGenMix_wide_dt[, dv_total := Coal + Diesel + Gas + Geo + Hydro + Solar + Wind + Wood]
nzGenMix_wide_dt[, dv_coal_gas := Coal + Gas]
nzGenMix_wide_dt[, dv_coal_gas_pc := 100 * dv_coal_gas/dv_total]
nzGenMix_wide_dt[, dv_solar_wind := Solar + Wind]
nzGenMix_wide_dt[, dv_solar_wind_pc := 100 * dv_solar_wind/dv_total]

# keep the vars we want for clarity
temp <- nzGenMix_wide_dt[, .(dv_dateTime, dv_coal_gas, dv_solar_wind,
                    dv_coal_gas_pc, dv_solar_wind_pc, dv_total)]

temp[, dv_date := lubridate::date(dv_dateTime)]

# aggregate to daily data for plotting
plotDT <- temp[,
               .(mean_dv_solar_wind_pc = mean(dv_solar_wind_pc),
                 mean_dv_coal_gas_pc = mean(dv_coal_gas_pc),
                 total_dv_coal_gas = sum(dv_coal_gas),
                 total_dv_solar_wind = sum(dv_solar_wind),
                 total_dv_total = sum(dv_total),
                 nObs = .N), # to check for days with < 48 half hours
               keyby = .(dv_date)
               ]
plotDT[, dv_year := lubridate::year(dv_date)] # for plots
plotDT[, total_dv_coal_gas_pc := 100 * total_dv_coal_gas/total_dv_total] # daily %
plotDT[, total_dv_solar_wind_pc := 100 * total_dv_solar_wind/total_dv_total]

message("Check for days with less than 48 hours - this will be truncated data due to DST breaks. We hate DST breaks")

## Check for days with less than 48 hours - this will be truncated data due to DST breaks. We hate DST breaks

table(plotDT$nObs)

## 
##   46   48 
##    4 1609

Figure ?? shows the mean half-hourly % generation by each type per day. This is slightly convoluted - it is the mean of the sum of the 48 daily half-hourly values. Unfold the code above for clarity.

The smoothed curves are estimated for each year. The lines terminate at the maximum value for the year. I’m still trying to decide if they tell us anything useful.

ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = mean_dv_solar_wind_pc, 
                            y = mean_dv_coal_gas_pc,
                            colour = as.factor(dv_year),
                            alpha = dv_year)) +
  geom_point() +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  guides(alpha = "none") +
  labs(x = "Solar & wind (mean % of half-hourly generation per day)",
       y = "Coal & gas (mean % of half-hourly generation per day)",
       caption = rmdParams$plotCap)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure 4.1: Mean half-hourly % generation by each type per day

# save it
ggplot2::ggsave(filename = "meanNZrenewablesVsfossilHalfHourPC.png", 
                path = rmdParams$plotPath,
                height = 5)

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure ?? shows the percentage of daily generation by type. This is less convoluted as it is the sum of generation per day for the two categories (solar + wind vs gas + coal) as a % of total daily generation.

Again the smoothed curve is estimated for each year.

ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = 100 * total_dv_solar_wind_pc, 
                            y = 100 * total_dv_coal_gas_pc,
                            colour = as.factor(dv_year),
                            alpha = dv_year)) +
  geom_point() +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  guides(alpha = "none") +
  labs(x = "Solar & wind (% of total daily generation)",
       y = "Coal & gas (% of total daily generation)",
       caption = rmdParams$plotCap)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure 4.2: Percentage of daily generation by type

ggplot2::ggsave(filename = "dailyNZpcGenMix.png", 
                path = rmdParams$plotPath,
                height = 5)

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

4.1 Half-hourly versions of the plot

Just cos we can… helpfully split into ‘peak’ and ‘off peak’ periods.

Peak period definitions:

Morning 07:00 - 09:00
Daytime 09:00 - 16:00
Evening 16:00 - 21:00
Night - all other times

Again the smoothed curve is estimated for each year (and demand period).

ggplot2::ggplot(nzGenMix_wide_dt[dv_year > 2011], aes(x = dv_solar_wind_pc, 
                            y = dv_coal_gas_pc,
                            alpha = dv_year,
                            colour = as.factor(dv_year))) +
  geom_point() +
  facet_wrap(. ~ dv_peakPeriod) +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  guides(alpha = "none") +
  labs(x = "Solar & wind (% of half-hourly generation)",
       y = "Coal & gas (% of half-hourly generation)",
       caption = rmdParams$plotCap)

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Figure 4.3: Percentage of half-hourly generation by type

ggplot2::ggsave(filename = "halfHourlyNZ_PCgenByPeakPeriod.png", 
                path = rmdParams$plotPath,
                height = 5)

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

5 MWh generation trends

Values are in kWh per half hour https://www.emi.ea.govt.nz/Wholesale/Datasets/Generation/Generation_MD

plotDT <- nzGenMix_dt[, 
                  .(dailyMWh = sum(as.numeric(kWh/1000))), # MWh -> MWh
                  keyby = .(dv_year, dv_date, dv_season, Fuel_Code)]

# annual sum for cross-check 
t <- plotDT[, .(sum_TWh = sum(dailyMWh/1000000)), keyby = .(Year = dv_year)]
t[, Year := as.factor(Year)]
gridCarbon::make_flexTable(t, caption = "Total TWh per year for cross-check")

``` ```{=html}

(\#tab:longTrendsDaily)Total TWh per year for cross-check
Year	sum_TWh
2020	40.6
2021	41.0
2022	40.7
2023	40.6
2024	16.9

# double check

Figure 5.1 shows individual trends.

p <- ggplot2::ggplot(plotDT, aes(x = dv_date, y = dailyMWh/1000, colour = Fuel_Code)) +
  geom_line() +
  facet_grid(Fuel_Code ~ .) +
  scale_color_viridis_d(name = "Source") +
  labs(x = "Date",
       y = "GWh",
       caption = rmdParams$plotCap)
p

Figure 5.1: Trends in daily GWh generation

plotly::ggplotly(p)

Figure 5.1: Trends in daily GWh generation

ggplot2::ggsave(filename = "dailyNZGenBySource.png", 
                path = rmdParams$plotPath,
                height = 5)

## Saving 7 x 5 in image

Figure 5.2 stacks them

ggplot2::ggplot(plotDT, aes(x = dv_date, y = dailyMWh/1000, fill = Fuel_Code)) +
  geom_col(position = "stack") +
  scale_fill_viridis_d(name = "Source") +
  labs(x = "Date",
       y = "GWh",
       caption = rmdParams$plotCap)

Figure 5.2: Trends in daily GWh generation (stacked)

ggplot2::ggsave(filename = "genNZTrendStackBySource.png", 
                path = rmdParams$plotPath
                )

## Saving 7 x 5 in image

5.1 Renewable %

Defined how?

Renewable = wind + solar + hydro + geothermal + wood - but note that geothermal is not necessarily low carbon

nzGenMix_wide_dt[, RENEWABLE := Geo + Hydro + Solar + Wind + Wood]

nzGenMix_wide_dt[, RENEWABLE_perc := 100*RENEWABLE/dv_total]

make_NgesoPlots <- function(dt, 
                            var = "RENEWABLE_perc", # defaults
                            lowColour = "green", 
                            highColour = "red", 
                            scaleLab = "Change me!", 
                            minMax = "max"){
  res <- list() # for the results
  res$tile <- ggplot2::ggplot(dt, aes(x = dv_date, 
                                      y = dv_hms, fill = get(var))) +
    geom_tile() +
    theme(legend.position = "bottom") +
    scale_x_date(date_labels="%b %Y",date_breaks  ="12 month") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    scale_fill_continuous(name = scaleLab,
                          high = highColour,
                          low = lowColour) +
    labs(x = "Date",
         y = "Half-hour",
         caption = rmdParams$plotCap)
  
  # if(minMax = "max"){
  #   res$tile <- res$tile + 
  #     scale_fill_continuous(name = paste0("Maximum ", scaleLab)
  #                           )
  # }
  #   if(minMax = "min"){
  #   res$tile <- res$tile + 
  #     scale_fill_continuous(name = paste0("Half-hourly ", scaleLab)
  #                           )
  #   }
  
  # line plot
  plotDT <- dt[,.(max = max(get(var)),
                  mean = mean(get(var)),
                  min = min(get(var))
  ),
  keyby = .(dv_date)]
  
  
  
  p <- ggplot2::ggplot(plotDT, aes(x = dv_date)) +
    theme(legend.position = "bottom") +
    scale_x_date(date_labels="%b %Y",date_breaks  ="12 month") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))
  
  if(minMax == "max"){
    yh <- max(plotDT$max)
    p <- p + geom_line(aes(y = max, colour = max)) +
      scale_color_continuous(name = paste0("Maximum half-hourly ", scaleLab),
                             high = highColour,
                             low = lowColour) +
      geom_smooth(aes(y = max), colour = "grey") +
      geom_hline(yintercept = yh, colour = highColour) +
      annotate("text", x = mean(plotDT$dv_date), 
                      y = yh*1.05, label = paste0("Maximum: ", round(yh)))
    p <- p + labs(x = "Date",
                  y = paste0("Maximum half-hourly ", scaleLab),
                  caption = rmdParams$plotCap
    )
  }
  
  if(minMax == "min"){
    yh <- min(plotDT$min)
    p <- p + geom_line(aes(y = min, colour = min)) +
      scale_color_continuous(name = paste0("Minimum half-hourly ", scaleLab),
                             high = highColour,
                             low = lowColour) +
      geom_smooth(aes(y = min), colour = "grey") + 
  geom_hline(yintercept = yh, colour = lowColour) +
      annotate("text", x = mean(plotDT$dv_date), 
                      y = yh*1.05, label = paste0("Minimum: ", round(yh)))
    p <- p + labs(x = "Date",
                  y = paste0("Minimum half-hourly ", scaleLab),
                  caption = rmdParams$plotCap
    )
  }
  res$line <- p
  
  return(res)
}

rpc <- make_NgesoPlots(nzGenMix_wide_dt, 
                       var = "RENEWABLE_perc",
                       lowColour = "red",
                       highColour = "green",
                       scaleLab = "Renewable generation (%)",
                       minMax = "max" # max line or min line?
)
rpc$tile

rpc$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

rpc <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2022], 
                       var = "RENEWABLE_perc",
                       lowColour = "red",
                       highColour = "green",
                       scaleLab = "Renewable generation (%)",
                       minMax = "max"
)
rpc$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

rpc$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

wpc <- nzGenMix_wide_dt[, .(mean_pc = mean(RENEWABLE_perc)), keyby = .(dv_hms, 
                                                                  Year = dv_year)]

ggplot2::ggplot(wpc, aes(x = dv_hms, alpha = Year, group = Year, y = mean_pc)) +
  geom_line() +
  labs(x = "Time of day",
       y = "% renewable generation")

5.2 Wind %

#plotDT <- gbGenMix_dt[, .()]
nzGenMix_wide_dt[, WIND_perc := 100*Wind/dv_total]
ci <- make_NgesoPlots(nzGenMix_wide_dt, 
                      var = "WIND_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Wind %",
                      minMax = "max"
)
ci$tile

ci$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

wpc <- nzGenMix_wide_dt[, .(mean_pc = mean(WIND_perc)), keyby = .(dv_hms, 
                                                                  Year = dv_year)]

ggplot2::ggplot(wpc, aes(x = dv_hms, alpha = Year, group = Year, y = mean_pc)) +
  geom_line() +
  labs(x = "Time of day",
       y = "% wind generation")

ci <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2022], 
                      var = "WIND_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Wind %",
                      minMax = "max"
)
ci$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

ci$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

5.3 Solar %

#plotDT <- gbGenMix_dt[, .()]
nzGenMix_wide_dt[, SOLAR_perc := 100*Solar/dv_total]
ci <- make_NgesoPlots(nzGenMix_wide_dt, 
                      var = "SOLAR_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Solar %",
                      minMax = "max"
)
ci$tile

ci$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

wpc <- nzGenMix_wide_dt[, .(mean_pc = mean(SOLAR_perc)), keyby = .(dv_hms, 
                                                                  Year = dv_year)]

ggplot2::ggplot(wpc, aes(x = dv_hms, alpha = Year, group = Year, y = mean_pc)) +
  geom_line() +
  labs(x = "Time of day",
       y = "% solar generation")

ci <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2022], 
                      var = "SOLAR_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Wind %",
                      minMax = "max"
)
ci$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

ci$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

5.4 Hydro %

#plotDT <- gbGenMix_dt[, .()]
nzGenMix_wide_dt[, HYDRO_perc := 100*Hydro/dv_total]
ci <- make_NgesoPlots(nzGenMix_wide_dt, 
                      var = "HYDRO_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Solar %",
                      minMax = "max"
)
ci$tile

ci$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

wpc <- nzGenMix_wide_dt[, .(mean_pc = mean(HYDRO_perc)), keyby = .(dv_hms, 
                                                                  Year = dv_year)]

ggplot2::ggplot(wpc, aes(x = dv_hms, alpha = Year, group = Year, y = mean_pc)) +
  geom_line() +
  labs(x = "Time of day",
       y = "% hydro generation")

ci <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2022], 
                      var = "HYDRO_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Hydro %",
                      minMax = "max"
)
ci$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

ci$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

5.5 Geothermal %

#plotDT <- gbGenMix_dt[, .()]
nzGenMix_wide_dt[, GEO_perc := 100*Geo/dv_total]
ci <- make_NgesoPlots(nzGenMix_wide_dt, 
                      var = "GEO_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Solar %",
                      minMax = "max"
)
ci$tile

ci$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

wpc <- nzGenMix_wide_dt[, .(mean_pc = mean(GEO_perc)), keyby = .(dv_hms, 
                                                                  Year = dv_year)]

ggplot2::ggplot(wpc, aes(x = dv_hms, alpha = Year, group = Year, y = mean_pc)) +
  geom_line() +
  labs(x = "Time of day",
       y = "% geothermal generation")

ci <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2022], 
                      var = "GEO_perc",
                      lowColour = "red",
                      highColour = "green",
                      scaleLab = "Geothermal %",
                      minMax = "max"
)
ci$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

ci$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

6 Carbon intensity trends

XX need to create a half-hourly CARBON_INTENSITY variable XX Requires: * per-fuel CI

Currently: https://environment.govt.nz/assets/publications/Measuring-emissions-guidance-August-2022/Emission-factors-workbook-Measuring-emissions-guidance-August-2022.xlsx

“The grid-average emission factor best reflects the carbon dioxide equivalent emissions associated with the generation of a unit of electricity purchased from the national grid in New Zealand in 2020”

“We calculate purchased electricity emission factors on a calendar-year basis and based on the average grid mix of generation types for calendar years. The emission factor accounts for the emissions from fuel combustion at thermal power stations and fugitive emissions from the generation of geothermal electricity. Thermal electricity is generated by burning fossil fuels.

The emission factor for purchased grid-average electricity does not include transmission and distribution losses. ”

“This emission factor also doesn’t reflect the real-world factors that influence the carbon intensity of the grid such as time of year, time of day and geographical area. Therefore, a grid -average emission factor may over or underestimate your organisation’s GHG emissions.”

2020 value = 0.120 kg CO2/kWh

Instead use a model derived from the GB MWh generation mix for 2023 (which may be way off but…)

Coefficients: * Intercept: 41.231171 * GAS_perc: 3.571570 * COAL_perc: 10.050410 * WIND_perc: -0.451285 * HYDRO_perc: 1.147793 # why is hydro +ve? * SOLAR_perc: -0.335122

This is (obviously) completely broken as it does not include a coefficient for Geothermal or Wood and the GB mix includes Nuclear, Interconnection and Storage. #YMMV

Need to fix :-)

# should be annual and fuel specific
# 2020 value

nzGenMix_wide_dt[, COAL_perc := Coal/dv_total]
nzGenMix_wide_dt[, GAS_perc := Gas/dv_total]
nzGenMix_wide_dt[, COAL_perc := Coal/dv_total]

nzGenMix_wide_dt[, CARBON_INTENSITY := 41.231171 +
                   GAS_perc   *  3.571570   + 
                   COAL_perc   * 10.050410   +
                   WIND_perc  * -0.451285  +
                   HYDRO_perc *  1.147793 + # why is hydro +ve?
                   SOLAR_perc * -0.335122]

summary(nzGenMix_wide_dt$CARBON_INTENSITY)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   66.26  100.38  106.94  106.58  113.15  136.09

6.1 Overall trends

Seasons:

Summer: Jun - Aug
Autumn: Sept - Nov
Winter: Dec - Feb
Spring: Mar - May

plotDT <- nzGenMix_wide_dt[, .(mean_CI = mean(CARBON_INTENSITY)), keyby = .(dv_year, dv_date, dv_season)]

ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = dv_date, y = mean_CI, colour = dv_season)) +
  geom_point() +
  scale_color_viridis_d(name = "Season") +
  theme(legend.position = "bottom") +
  labs(x = "Date",
       y = "Mean g CO2e/kWh",
       caption = rmdParams$plotCap)

Figure 6.1: Mean half-hourly carbon intensity per day

ggplot2::ggsave(filename = "meanCiTrendBySeason.png", 
                path = rmdParams$plotPath
                )

## Saving 7 x 5 in image

Re-draw as a boxplot of mean daily CI by month - plotted at month start. 6.2 shows outliers nicely.

plotDT <- nzGenMix_wide_dt[, .(mean_CI = mean(CARBON_INTENSITY)), 
                     keyby = .(dv_year, 
                               dv_date,
                               dv_season)]
plotDT[, dv_month := lubridate::floor_date(dv_date, unit = "months"),]
ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = dv_month, 
                                            y = mean_CI, 
                                            group = dv_month,
                                            colour = dv_season)) +
  geom_boxplot() +
  scale_color_viridis_d(name = "Season") +
  theme(legend.position = "bottom") +
  labs(x = "Month",
       y = "Mean g CO2e/kWh",
       caption = rmdParams$plotCap)

Figure 6.2: Mean daily CI boxplots per month

ggplot2::ggsave(filename = "meanCiTrendByMonthSeason.png", 
                path = rmdParams$plotPath
                )

## Saving 7 x 5 in image

Figure 6.3 shows half-hourly carbon intensity over time.

#plotDT <- nzGenMix_wide_dt[, .()]
ci <- make_NgesoPlots(nzGenMix_wide_dt, 
                      var = "CARBON_INTENSITY",
                      lowColour = "green",
                      highColour = "brown",
                      scaleLab = "Carbon intensity (g CO2/MW)",
                      minMax = "min"
)
ci$tile

Figure 6.3: Half-hourly carbon intensity over time

ci$line

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

ci <- make_NgesoPlots(nzGenMix_wide_dt[dv_year > 2021], 
                      var = "CARBON_INTENSITY",
                      lowColour = "green",
                      highColour = "brown",
                      scaleLab = "Carbon intensity (g CO2/MW)",
                      minMax = "min"
)
ci$tile +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.

ci$line +
  scale_x_date(date_labels="%b-%Y",date_breaks  ="3 month")

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

6.2 Annual by peak period

Mean carbon intensity per year and season within peak period.

Figure 6.4 suggests evening peak periods still have slightly higher carbon intensity and the shape of the reduction curves differ by season although rather less by period. Interestingly the sustained reduction in carbon intensity in Summer has leveled off.

plotDT <- nzGenMix_wide_dt[, .(mean_CI = mean(CARBON_INTENSITY)), keyby = .(dv_year, dv_peakPeriod, dv_season)]

ggplot2::ggplot(plotDT, aes(x = dv_year, y = mean_CI, colour = dv_peakPeriod)) +
  geom_line() +
  scale_color_viridis_d(name = "Peak period") +
  facet_wrap(. ~ dv_season) +
  labs(x = "Year",
       y = "Mean g CO2e/kWh",
       caption = rmdParams$plotCap)

Figure 6.4: Mean half-hourly carbon intensity by peak period and season 2012-2022

ggplot2::ggsave(filename = "annualMeanCIByPeak.png", 
                path = rmdParams$plotPath
                )

## Saving 7 x 5 in image

7 Renewables and overall generation

Do we see a relationship between renewables generating and peak demand? This will be mediated by the way the electricity market works.

We may find wind curtailment (not visible here) at low demand periods where nuclear can’t be shut off.

First, what is the general average shape of carbon intensity and renewable generation?

Figure 7.1 shows that although the mean half-hourly carbon intensity had fallen over time (with the effect of solar in summer particularly noticeable), the morning and evening peaks are still relatively more carbon intense as demand overtakes the available renewable supply.

plotDT <- nzGenMix_wide_dt[dv_year > 2011, .(mean_ci = mean(CARBON_INTENSITY),
                          mean_renewables_MW = mean(RENEWABLE),
                          mean_renewables_pc = mean(RENEWABLE_perc)),
                          keyby = .(dv_year, dv_hms, dv_season)]

ggplot2::ggplot(plotDT, aes(x = dv_hms, y = mean_ci, 
                            alpha = dv_year,
                            colour = dv_year,
                            group = dv_year)) +
  geom_line() +
  scale_alpha_continuous(name="Year") +
  scale_color_continuous(name = "Year",
                         low = "grey", 
                         high = "#3CBAC6") + # UoS from Marine palette
  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  facet_wrap(. ~ dv_season) +
  labs(x = "Time of day",
       y = "Mean half-hourly carbon intensity",
       caption = rmdParams$plotCap)

Figure 7.1: Mean half-hourly carbon intensity by year and season

ggplot2::ggplot(plotDT, aes(x = dv_hms, y = mean_renewables_MW/1000, 
                            colour = dv_year, 
                            alpha = dv_year,
                            group = dv_year)) +
  geom_line() +
  scale_alpha_continuous(name = "Year") +
  scale_color_continuous(name = "Year",
                         low = "grey", 
                         high = "#3CBAC6") + # UoS from Marine palette
  facet_grid(dv_season ~ .) +
    labs(x = "Time of day",
       y = "Mean renewables (GW)",
       caption = rmdParams$plotCap)

Figure 7.2: Mean half-hourly renewable generation by year and season

ggplot2::ggplot(plotDT, aes(x = dv_hms, y = mean_renewables_pc, 
                            colour = dv_year, 
                            alpha = dv_year,
                            group = dv_year)) +
  geom_line() +
  scale_alpha_continuous(name = "Year") +
  scale_color_continuous(name = "Year",
                         low = "grey", 
                         high = "#3CBAC6") + # UoS from Marine palette
  facet_grid(dv_season ~ .) +
    labs(x = "Time of day",
       y = "Mean % renewables (%)",
       caption = rmdParams$plotCap)

Figure 7.3: Mean half-hourly renewable generation by year and season

ggplot2::ggplot(nzGenMix_wide_dt, aes(x = RENEWABLE, y = dv_total)) +
  geom_point() +
  facet_wrap(. ~ dv_year) +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

7.4 shows the rise of both wind and solar (mid-day peak)…

Beware incomplete yearss

nzGenMix_wide_dt[, GENERATION := dv_total]
plotDT <- nzGenMix_wide_dt[, .(mean_renewables = mean(RENEWABLE),
                          mean_solar = mean(Solar),
                 mean_generation = mean(GENERATION)),
             keyby = .(dv_year, dv_hms, dv_peakPeriod, dv_season)]

ggplot2::ggplot(plotDT[dv_year > 2016], aes(x = dv_hms, 
                                         colour = dv_peakPeriod,
                                         alpha = dv_year,
                                         group = dv_year)) +
  geom_line(aes(y = mean_renewables/1000)) +
  scale_alpha_continuous(name = "Year") +
  scale_color_discrete(name = "Peak period") +
  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  facet_wrap(. ~ dv_season) +
  labs(x = "Time of day",
       y = "Mean renewables (GW)",
       caption = rmdParams$plotCap)

Figure 7.4: Trends in mean half-hourly renewable generation by time of day and season

What would have happened if we increased solar generation in 2022 by a factor of 10? We would have needed some storage in Spring & Summer…

ggplot2::ggplot(plotDT[dv_year == 2022], aes(x = dv_hms,
                                             colour = dv_peakPeriod,
                                             group = dv_peakPeriod)) +
  geom_point(aes(y = mean_generation/1000)) +
  geom_line(aes(y = mean_solar/1000 * 10), linetype = "dotdash") +
  scale_color_discrete(name = "Peak period") +
    theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  facet_wrap(. ~ dv_season) +
  labs(x = "Time of day",
       y = "Mean generation (GW)",
       caption = rmdParams$plotCap)

Figure 7.5: Comparing mean total generation & 10 * solar generation by time of day for 2020

YMMV on 7.6

plotDT <- nzGenMix_wide_dt[, .(mean_renewables = mean(RENEWABLE),
                 mean_generation = mean(GENERATION)),
             keyby = .(dv_year, dv_hms, dv_peakPeriod, dv_season)]

ggplot2::ggplot(plotDT[dv_year > 2016], aes(x = mean_generation/1000 , y = mean_renewables/1000,
                                         colour = dv_peakPeriod)) +
  geom_point() +
  scale_color_discrete(name = "Period") +
  facet_grid(dv_season ~ dv_year) +
  labs(x = "Mean total generation (GW)",
       y = "Mean renewables (GW)",
       caption = rmdParams$plotCap)

Figure 7.6: Mean renewables vs Mean total generation

8 Summary

That’s it.

You might want to look at recent academic research on this topic:

(Staffell 2017)
(Staffell and Pfenninger 2018)

9 Annex

9.1 Data descriptors

9.1.1 NZ gen mix data

skimr::skim(nzGenMix_wide_dt)

Table 9.1: Data summary
Name	nzGenMix_wide_dt
Number of rows	77416
Number of columns	32
Key	dv_dateTime
_______________________
Column type frequency:
Date	1
difftime	2
factor	2
numeric	26
POSIXct	1
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_date	0	1	2020-01-01	2024-05-31	2022-03-17	1613

Variable type: difftime

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_hms	0	1	900 secs	85500 secs	42300 secs	48
hms	0	1	900 secs	85500 secs	42300 secs	48

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
dv_season	0	1	FALSE	4	Aut: 22080, Sum: 20208, Win: 17664, Spr: 17464
dv_peakPeriod	0	1	FALSE	5	Ear: 22582, Day: 22582, Eve: 12904, Lat: 12896

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Coal	1	97716.97	93256.93	0.00	0.00	79688.00	161903.00	365201.00	▇▅▂▂▁
Diesel	1	361.80	3439.98	0.00	0.00	0.00	0.00	79213.00	▇▁▁▁▁
Gas	1	254654.88	125511.52	18133.00	167618.50	245349.00	334624.50	646409.00	▃▇▆▂▁
Geo	1	424445.46	31472.62	224851.00	409428.00	430211.00	445384.50	548599.00	▁▁▃▇▁
Hydro	1	1379334.99	343833.61	492522.00	1110369.25	1396932.00	1626214.50	2363701.00	▂▆▇▅▁
Solar	1	79.75	749.31	0.00	0.00	0.00	0.00	10920.00	▇▁▁▁▁
Wind	1	152139.19	96910.12	0.00	70422.75	138378.00	221762.25	446238.00	▇▇▆▃▁
Wood	1	13058.15	5454.13	0.00	12047.00	15243.00	16652.25	19561.00	▂▁▁▆▇
dv_year	1	2021.73	1.29	2020.00	2021.00	2022.00	2023.00	2024.00	▇▇▇▇▃
dv_month	1	6.19	3.47	1.00	3.00	6.00	9.00	12.00	▇▆▅▅▆
dv_hour	1	11.50	6.92	0.00	5.00	11.00	17.00	23.00	▇▇▆▇▇
dv_total	1	2321791.20	401583.73	1324732.00	1983700.50	2353673.00	2600489.75	3626574.00	▂▆▇▂▁
dv_coal_gas	1	352371.85	181359.86	19460.00	208045.00	332673.00	482707.00	962020.00	▆▇▆▂▁
dv_coal_gas_pc	1	14.89	6.71	1.02	9.61	14.81	19.97	35.68	▃▇▇▃▁
dv_solar_wind	1	152218.95	96918.56	0.00	70534.75	138392.00	221797.25	449415.00	▇▇▆▃▁
dv_solar_wind_pc	1	6.74	4.47	0.00	3.08	5.97	9.74	24.57	▇▆▃▁▁
RENEWABLE	1	1969057.55	336540.82	1143537.00	1701580.50	1988560.00	2214708.75	3062302.00	▂▆▇▃▁
RENEWABLE_perc	1	85.10	6.72	64.32	80.01	85.19	90.39	98.98	▁▃▇▇▃
WIND_perc	1	6.74	4.47	0.00	3.08	5.97	9.74	24.57	▇▆▃▁▁
SOLAR_perc	1	0.00	0.03	0.00	0.00	0.00	0.00	0.47	▇▁▁▁▁
HYDRO_perc	1	58.90	7.97	28.86	53.82	59.03	64.19	83.17	▁▂▇▆▁
GEO_perc	1	18.88	3.81	9.56	16.00	18.05	21.60	34.20	▂▇▅▂▁
COAL_perc	1	0.04	0.04	0.00	0.00	0.03	0.07	0.21	▇▃▂▁▁
GAS_perc	1	0.11	0.05	0.01	0.08	0.11	0.14	0.25	▃▇▇▃▁
CARBON_INTENSITY	1	106.58	9.92	66.26	100.38	106.94	113.15	136.09	▁▂▇▇▁
GENERATION	1	2321791.20	401583.73	1324732.00	1983700.50	2353673.00	2600489.75	3626574.00	▂▆▇▂▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_dateTime	0	1	2020-01-01 00:15:00	2024-05-31 23:45:00	2022-03-17 12:00:00	77416

9.2 R environment

Packages etc:

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
here (Müller 2017)
hms (Müller 2018)
knitr (Xie 2016b)
lubridate (Grolemund and Wickham 2011)
rmarkdown (Allaire et al. 2018)

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.

Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.

———. 2018. Hms: Pretty Time of Day. https://CRAN.R-project.org/package=hms.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Staffell, Iain. 2017. “Measuring the Progress and Impacts of Decarbonising British Electricity.” Energy Policy 102 (March): 463–75. https://doi.org/10.1016/j.enpol.2016.12.037.

Staffell, Iain, and Stefan Pfenninger. 2018. “The Increasing Impact of Weather on Electricity Supply and Demand.” Energy 145 (February): 65–78. https://doi.org/10.1016/j.energy.2017.12.051.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

NZ generation share trends

Electricity Authority (EA) data

Ben Anderson (dataknut@icloud.com)

Last run at: 2024-06-26 21:10:12.232106